Display Advertising has been the subject of rigorous research with extremely fast development during the past decade. The area has generated billions of revenue, originated hundreds of scientific papers and patents, saw a broad variety of implementations, yet the accuracy of prediction technologies leaves to desire more. The expected revenue from displaying each ad is a function of both the bid price and the Click-Through-Rate (CTR). Sponsored search advertising, contextual advertising, display advertising, and real-time bidding auctions have all relied heavily on the ability of learned models to predict ad CTR accurately, quickly and reliably. CTR prediction is not only related to revenue of web publishers but also experience of users and payment of advertisers, because this influences ranking, filtering, placement and pricing of ads. Campaign performance directly depends on how well the CTR can be estimated, whereas the performance optimization can be considered as the problem of accurately estimating CTR. If these quantities are over-estimated, bid prices will always be higher than what they should be, the advertiser will waste campaign budget on less valuable impressions; on the other hand, if these quantities are underestimated, the advertiser will miss high-value impressions that may have led to actions and the campaign will under deliver. Thus CTR prediction plays an important role in the multi-faceted advertising business. However, it is a big challenge to set up a flexible complete model frame-work that consistently integrates information from all dimensions, including users, publishers, and advertisers.
Two challenges are particularly important. First, CTR generally ranges from 0.001% to 0.5%, which is highly skewed towards the non-clicked class with very high variance. Predictions of CTR for ads are generally based on machine learning or statistical models trained by using the past click data.
Another cause of the complexity of Display Advertising is the huge event space, whose data hierarchy can be expressed as {Advertiser, Publisher, User}. Prior art predominantly describes separate efforts focused on just Advertiser, or Publisher, or User, because normally an integrated multi-dimensional framework being too large and too complex to handle.